Abstract
We propose a novel methodology that jointly estimates the proportions of skilled/unskilled funds and the cross-sectional distribution of skill in the mutual fund industry. We model this distribution as a three-component mixture of a point mass at zero and two components — one negative, one positive — that we estimate semi-parametrically. This generalizes previous approaches and enables information-sharing across funds in a data-driven manner. We find that the skill distribution is non-normal (asymmetric and fat-tailed). Furthermore, while the majority of funds have negative alpha, a substantial 13% generate positive alpha. Our approach improves out-of-sample portfolio performance and significantly alters asset allocation decisions.
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